Simulated Annealing Theory with Applications edited by Rui Chibante SCIYO Simulated Annealing Theory with Applications Edited by Rui Chibante Published by Sciyo Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2010 Sciyo All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited After this work has been published by Sciyo, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work Any republication, referencing or personal use of the work must explicitly identify the original source Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher No responsibility is accepted for the accuracy of information contained in the published articles The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book Publishing Process Manager Ana Nikolic Technical Editor Sonja Mujacic Cover Designer Martina Sirotic Image Copyright jordache, 2010 Used under license from Shutterstock.com First published September 2010 Printed in India A free online edition of this book is available at www.sciyo.com Additional hard copies can be obtained from publication@sciyo.com Simulated Annealing Theory with Applications, Edited by Rui Chibante   p.  cm ISBN 978-953-307-134-3 SCIYO.COM WHERE KNOWLEDGE IS FREE free online editions of Sciyo Books, Journals and Videos can be found at www.sciyo.com Contents Preface  VII Chapter Parameter identification of power semiconductor device models using metaheuristics  Rui Chibante, Armando Araújo and Adriano Carvalho Chapter Application of simulated annealing and hybrid methods in the solution of inverse heat and mass transfer problems  17 Antônio José da Silva Neto, Jader Lugon Junior, Francisco José da Cunha Pires Soeiro, Luiz Biondi Neto, Cesar Costapinto Santana, Fran Sérgio Lobato and Valder Steffen Junior Chapter Towards conformal interstitial light therapies: Modelling parameters, dose definitions and computational implementation  51 Emma Henderson,William C Y Lo and Lothar Lilge Chapter A Location Privacy Aware Network Planning Algorithm for Micromobility Protocols  75 László Bokor, Vilmos Simon and Sándor Imre Chapter Simulated Annealing-Based Large-scale IP Traffic Matrix Estimation  99 Dingde Jiang, XingweiWang, Lei Guo and Zhengzheng Xu Chapter Field sampling scheme optimization using simulated annealing  113 Pravesh Debba Chapter Customized Simulated Annealing Algorithm Suitable for Primer Design in Polymerase Chain Reaction Processes  137 Luciana Montera, Maria Carmo Nicoletti, Said Sadique Adi and Maria Emilia Machado Telles Walter Chapter Network Reconfiguration for Reliability Worth Enhancement in Distribution System by Simulated Annealing  161 Somporn Sirisumrannukul VI Chapter Optimal Design of an IPM Motor for Electric Power Steering Application Using Simulated Annealing Method  181 Hamidreza Akhondi, Jafar Milimonfared and Hasan Rastegar Chapter 10 Using the simulated annealing algorithm to solve the optimal control problem  189 Horacio Martínez-Alfaro Chapter 11 A simulated annealing band selection approach for high-dimensional remote sensing images  205 Yang-Lang Chang and Jyh-Perng Fang Chapter 12 Importance of the initial conditions and the time schedule in the Simulated Annealing  217 A Mushy State SA for TSP Chapter 13 Multilevel Large-Scale Modules Floorplanning/Placement with Improved Neighborhood Exchange in Simulated Annealing  235 Kuan-ChungWang and Hung-Ming Chen Chapter 14 Simulated Annealing and its Hybridisation on Noisy and Constrained Response Surface Optimisations  253 Pongchanun Luangpaiboon Chapter 15 Simulated Annealing for Control of Adaptive Optics System  275 Huizhen Yang and Xingyang Li Preface This book presents recent contributions of top researchers working with Simulated Annealing (SA) Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine The book contains 15 research papers Chapters to address inverse problems or parameter identification problems These problems arise from the necessity of obtaining parameters of theoretical models in such a way that the models can be used to simulate the behaviour of the system for different operating conditions Chapter presents the parameter identification problem for power semiconductor models and chapter for heat and mass transfer problems Chapter discusses the use of SA in radiotherapy treatment planning and presents recent work to apply SA in interstitial light therapies The usefulness of solving an inverse problem is clear in this application: instead of manually specifying the treatment parameters and repeatedly evaluating the resulting radiation dose distribution, a desired dose distribution is prescribed by the physician and the task of finding the appropriate treatment parameters is automated with an optimisation algorithm Chapters and present two applications in Telecommunications field Chapter discusses the optimal design and formation of micromobility domains for extending location privacy protection capabilities of micromobility protocols In chapter SA is used for large-scale IP traffic matrix estimation, which is used by network operators to conduct network management, network planning and traffic detecting Chapter and present two SA applications in Geology and Molecular Biology fields, particularly the optimisation problem of land sampling schemes for land characterisation and primer design for PCR processes, respectively Some Electrical Engineering applications are analysed in chapters to 11 Chapter deals with network reconfiguration for reliability worth enhancement in electrical distribution systems The optimal design of an interior permanent magnet motor for power steering applications is discussed in chapter In chapter 10 SA is used for optimal control systems design and in chapter 11 for feature selection and dimensionality reduction for image classification tasks Chapters 12 to 15 provide some depth to SA theory and comparative studies with other optimisation algorithms There are several parameters in the process of annealing whose values affect the overall performance Chapter 12 focuses on the initial temperature and proposes a new approach to set this control parameter Chapter 13 presents improved approaches on the multilevel hierarchical floorplan/placement for large-scale circuits An VIII improved format of !-neighborhood and !-exchange algorithm in SA is used In chapter 14 SA performance is compared with Steepest Ascent and Ant Colony Optimization as well as an hybridisation version Control of adaptive optics system that compensates variations in the speed of light propagation is presented in last chapter Here SA is also compared with Genetic Algorithm, Stochastic Parallel Gradient Descent and Algorithm of Pattern extraction Special thanks to all authors for their invaluable contributions Editor Rui Chibante Department of Electrical Engineering, Institute of Engineering of Porto, Portugal Parameter identification of power semiconductor device models using metaheuristics 1 x Parameter identification of power semiconductor device models using metaheuristics Rui Chibante1, Armando Araújo2 and Adriano Carvalho2 Department of Electrical Engineering, Institute of Engineering of Porto Department of Electrical Engineering and Computers, Engineering Faculty of Oporto University Portugal Introduction Parameter extraction procedures for power semiconductor models are a need for researchers working with development of power circuits It is nowadays recognized that an identification procedure is crucial in order to design power circuits easily through simulation (Allard et al., 2003; Claudio et al., 2002; Kang et al., 2003c; Lauritzen et al., 2001) Complex or inaccurate parameterization often discourages design engineers from attempting to use physics-based semiconductor models in their circuit designs This issue is particularly relevant for IGBTs because they are characterized by a large number of parameters Since IGBT models developed in recent years lack an identification procedure, different recent papers in literature address this issue (Allard et al., 2003; Claudio et al., 2002; Hefner & Bouche, 2000; Kang et al., 2003c; Lauritzen et al., 2001) Different approaches have been taken, most of them cumbersome to be solved since they are very complex and require so precise measurements that are not useful for usual needs of simulation Manual parameter identification is still a hard task and some effort is necessary to match experimental and simulated results A promising approach is to combine standard extraction methods to get an initial satisfying guess and then use numerical parameter optimization to extract the optimum parameter set (Allard et al., 2003; Bryant et al., 2006; Chibante et al., 2009b) Optimization is carried out by comparing simulated and experimental results from which an error value results A new parameter set is then generated and iterative process continues until the parameter set converges to the global minimum error The approach presented in this chapter is based in (Chibante et al., 2009b) and uses an optimization algorithm to perform the parameter extraction: the Simulated Annealing (SA) algorithm The NPT-IGBT is used as case study (Chibante et al., 2008; Chibante et al., 2009b) In order to make clear what parameters need to be identified the NPT-IGBT model and the related ADE solution will be briefly present in following sections Simulated Annealing Theory with Applications Simulated Annealing Annealing is the metallurgical process of heating up a solid and then cooling slowly until it crystallizes Atoms of this material have high energies at very high temperatures This gives the atoms a great deal of freedom in their ability to restructure themselves As the temperature is reduced the energy of these atoms decreases, until a state of minimum energy is achieved In an optimization context SA seeks to emulate this process SA begins at a very high temperature where the input values are allowed to assume a great range of variation As algorithm progresses temperature is allowed to fall This restricts the degree to which inputs are allowed to vary This often leads the algorithm to a better solution, just as a metal achieves a better crystal structure through the actual annealing process So, as long as temperature is being decreased, changes are produced at the inputs, originating successive better solutions given rise to an optimum set of input values when temperature is close to zero SA can be used to find the minimum of an objective function and it is expected that the algorithm will find the inputs that will produce a minimum value of the objective function In this chapter’s context the goal is to get the optimum set of parameters that produce realistic and precise simulation results So, the objective function is an expression that measures the error between experimental and simulated data The main feature of SA algorithm is the ability to avoid being trapped in local minimum This is done letting the algorithm to accept not only better solutions but also worse solutions with a given probability The main disadvantage, that is common in stochastic local search algorithms, is that definition of some control parameters (initial temperature, cooling rate, etc) is somewhat subjective and must be defined from an empirical basis This means that the algorithm must be tuned in order to maximize its performance Fig Flowchart of the SA algorithm 278 Simulated Annealing Theory with Applications  (r ) and u (r ) are continuous functions ( r  {x, y} is a vector in the plane orthogonal to the optical axes) The AO system mainly includes a 61-element deformable mirror to correct the wave-front aberrations  (r ) , an imaging system to record the focal spot, a performance metric analyzer to calculate the system performance metric J from the data of focal spot, the simulated annealing algorithm to produce control signals u  {u1 , u2 , u61} for the 61- element deformable mirror according to changes of the performance metric J Fig Block diagram of simulation 3.2 Specifications of 61-element deformable mirror The phase compensation u (r ) , introduced by the deformable mirror, can be combined linearly with response functions of actuators: 61 u (r )   u j S j (r ) (1) j 1 Where u j is the control signal and S j (r ) is the response function of the j ' th actuator On the basis of real measurements, we know the response function of 61-element deformable mirror actuators is Gaussian distribution approximately (Jiang & etal 1991): S j (r )  S j ( x, y )  exp{ln [ ( x  x j )  ( y  y j ) / d ] } (2) Where ( x j , y j ) is the location of the j ' th actuator,  is the coupling value between actuators and is set to 0.08, d is the distance between actuators, and  is the Gaussian index and is set to Fig gives the location distribution of 61-element deformable mirror actuators The circled line in the figure denotes the effective aperture and the layout of all actuators is triangular Simulated Annealing for Control of Adaptive Optics System 279 Fig Distribution of actuators location of 61-element deformable mirror 3.3 Atmospheric turbulence conditions We use the method proposed by N Roddier, which makes use of a Zernike expansion of randomly weighted Karhunen-Loeve functions, to simulate atmospherically distorted wavefronts (Roddier, 1990) Considering that the low-order aberrations (tilts, defocus, astigmatism, etc) have the most significant impact on image quality, we use the first 104 Zernike polynomial orders Different phase screens generated according to this method are not correlated to each other and represent the Kolmogorov spectrum The phase screens are defined over 128  128 pixels which is also the grid of the wave-front corrector and don’t include the tip/tilt aberrations The tip/tilt aberrations are usually controlled by another control loop and are considered as being removed completely in our simulation Atmospheric turbulence strength for a receiver system with aperture size D can be characterized by the following two parameters: the ratio D / r0 and the averaged Strehl Ratio (SR) of phase fluctuations, where r0 is the turbulence coherence length and SR is defined as the ratio of the maximum intensities of the aberrated point spread function and the diffraction-limited point spread function Phase screens of different atmospheric turbulence strength can be obtained through changing r0 in the simulation program The correction capability of the AO system based on simulated annealing is analyzed when D / r0 is 10 and corresponding averaged SR is about 0.1 3.4 Considerations for the performance metric J Possible measures of energy spread in the focal plane that can be used as the energy function of simulated annealing J are: (1) The Strehl ratio(SR): SR  max( I (r )) / max( I dl (r )) , (3) where I ( r ) is the intensity distribution of focal plane with the turbulence and I dl ( r ) is the diffraction-limited laser intensity distribution achievable in the absence of the turbulence This quantity is not difficult to measure on the focal plane It does not seem to be very informative because it does not account for the whole intensity distribution We use the SR as the reference of correction capability in the text 280 Simulated Annealing Theory with Applications (2) The encircled energy (EE): EE   I (r )dr (4)  where  is a region with a laser hot spot where maximum energy is to be collected This metric also does not necessarily take into account the whole intensity distribution In addition, it depends on the choice of area  (3) Image sharpness ( IS mn ) used in various active imaging applications: ISmn   |  m  n I (r ) | dr ,  m x n y (m  n)  0, 1, (5) This quantity is relatively simple to measure, and is intuitively appealing since smaller tighter intensity distributions have wider spatial frequency spectrum and, therefore, larger sharpness (4) The mean radius (MR): MR   | r  r | I (r )d  I (r )d r 2 r  rI (r )d r  I (r )d r , r  (6) where r is the intensity distribution centroid MR can be easily measured either by a single photodetector with a special mask attached to it or by postprocessing a matrix detector output This measure appears to be the most attractive one for it gives straightforward mathematical meaning to the idea of energy spread, it is nonparametric, and it accounts for the whole intensity distribution For imaging applications metrics 1-3 are proven (Muller & Buffington, 1974) to attain their global maxima for the diffraction-limited image It is clear that the global minimum of the MR metric corresponds to the smallest energy spread It is also possible to invent other functions, including vector functions, as well as to create compound cost functions with additional penalty terms All these possibilities deserve thorough investigation However, only the MR metric is used in our simulations The relationship between the performance metric J and control parameters {u j } is J  J [ (r )]  J [ (r )  u (r )] (7) J  J (u1 , u2 , u61 ) can be considered as the non-linear function of 61 control signals because the  ( r ) keeps unchanged during a relatively short time In real applications, we can get the performance metric data from the photoelectric detector, for example from a CCD or a pinhole, and then define different performance metrics based on different applications Simulated Annealing for Control of Adaptive Optics System 281 3.5 Descriptions of other stochastic parallel optimization algorithms (1) SPGD (Vorontsov & Carhart, 2000) control is a “hill-climbing” technique implemented by the direct optimization of a system performance metric applied through an active optical component Control is based on the maximization (or, with equal complexity minimization) of a system performance metric by small adjustments in actuator displacement in the mirror array SPGD requires small random perturbations u  {u1 , u2 , u61} with fixed amplitude | u j   | and random signs with equal probabilities for Pr(u j   )  0.5 (Spall, 1992), to be applied to all 61 deformable mirror control channels simultaneously Then for a given single random u , the control signals are updated with the rule: u ( k 1)  u ( k )  u ( k ) J ( k ) (8) where  is a gain coefficient which scales the size of the control parameters Note that nonBernoulli perturbations are also allowed in the algorithm, but one must be careful that the mathematical conditions (Spall, 1992) are satisfied SPGD follows the rule during the iteration of algorithm: ( ( J ( k )  J k )  J k ) (9) ( J k )  J (u ( k )  u ( k ) ) (10.a) ( J k )  J (u ( k )  u ( k ) ) where (10.b) From the introduction to SPGD, we know there are only two parameters to be adjusted in algorithm: one is perturbation amplitude  and the other is gain coefficient  (2) GA is a kind of evolutionary computation, which represents a class of stochastic search and optimization algorithms that use a Darwinian evolutionary model, adopts the concept of survival of the fittest in evolution to find the best solution to some multivariable problem, and includes mainly three kinds of operation in every generation: selection, crossover and mutation GA works with a population of candidate solutions and randomly alters the solutions over a sequence of generations according to evolutionary operations of competitive selection, mutation and crossover The fitness of each population element to survive into the next generation is determined by a selection scheme based on evaluating the performance metric function for each element of the population The selection scheme is such that the most favourable elements of the population tend to survive into the next generation while the unfavourable elements tend to perish The control vector {u j } was considered as the individual to be evolved and the performance metric is called as fitness function After the initial population is made according to the roulette selection principle, excellent individuals are selected from the population with a ratio rs Then new individuals are obtained by randomly crossing the chromosomes of the old individuals with a probability of Pc Finally, some chromosome positions of individuals are mutated randomly with a mutation rate of Pm for introducing a new individual By going 282 Simulated Annealing Theory with Applications through above process, GA will gradually find the optimum mirror shape that can yield the best fitness Parameter rs , Pc and Pm are set at 0.2, 0.65 and 0.65 accord to the corresponding reference value (Chen, etal 1996) The population size N and the number of evolving generation L are needed to adjust (3) Alopex is a stochastic correlative learning algorithm which updates the control parameters by making use of correlation between the variations of control parameters and the variations of performance metric without needing (or explicitly estimating) any derivative information Since its introduction for mapping visual receptive fields (Harth & Tzanakou, 1974), it has subsequently been modified and used in many applications such as models of visual perception, pattern recognition, and adaptive control, learning in neural networks, and learning decision trees Empirically, the Alopex algorithm has been shown to be robust and effective in many applications We used a two timescale version Alopex, called as 2t-Alopex (Roland, etal 2002) The control signals are updated according to the rules: u ( k 1)  u ( k )  u ( k ) (11)  probability p(k) u ( k )   probability 1-p(k) 1 p ( k )  p ( k 1)   (  ( k )  p ( k 1) ) (13)  (14) (k ) (k )  1/(1  exp(u J (k ) / T (k ) )) (12) T ( k ) in equation (12) is a “temperature” parameter updated every M iterations(for a suitably chosen M ) using the following annealing schedule: T (k ) T ( k 1) if k is not a multiple of M   k (k' ) otherwise  M '  | J |  k k M (15) Where  and  are step-size parameters such that   o( ) There are at least two parameters to be adjusted:  and  Results and Analysis We perform the adaptation process over 100 phase realizations The averaged evolution curves, the standard deviation evolution curves of the metric and corresponding SR evolution curves are the recorded simulation results 4.1 Selection of different algorithm parameters Every algorithm has its rational limit of parameters for a given application We select the most optimal parameters of every algorithm through large numbers of simulation tests when D / r0 is 10 Simulated Annealing for Control of Adaptive Optics System 283 In SA, the adjustment coefficient  and the cooling rate  are main factors for convergence rate and correction effect and we set  was 0.15 and  was 0.98 The key parameters of Alopex are step-size parameters  and  , and we set  was 0.03 and  was 0.55 The amplitude  and the gain coefficient  are two main factors which affect convergence rate and correction capability of SPGD For a fixed  , there exists an optimal range for  Too small  will cause too slow convergence rate, while too big  will make the algorithm trap into local extrumums and the evolution curve of performance metric appears dither We find the effective range of  is within 0.01-1.5 for SPGD We fixed the same  at 0.2 for SPGD, r is set at After probability parameters rs , Pc and Pm in GA are established on the basis of experience, the convergence rate is affected by the population size N and the number of evolution generation L For the same correction effect, L will be fewer if N is bigger, while the algorithm will need more times of evolution when N is smaller If GA not only converge rapidly but also has good correction effect, it’s necessary to balance N and L We set N at 100 and L at 500 in simulation 4.2 Adaptation process In order to converge completely, we set the iteration number of algorithms respectively SA is set at 4000 times, GA 500 generations, Alopex 4000 times and SPGD 1500 times The averaged evolution curves, the standard deviation (SD) evolution curves of the metric MR over 100 phase realizations and corresponding averaged SR evolution curves are given in Fig 5, Fig 6, Fig and Fig 8, in which the value of MR is normalize by that of diffractionlimited focal plane and the standard deviation(SD) is calculated as follows: SD   ( J   J ) 1/ J (16) Fig to Fig show simulation results when SA, GA, SPGD and Alopex are use to control the AO system respectively Averaged curves of MR are given in Fig 5(a) to Fig 8(a), in which averaged evolution curves are normalized to be in the optimal case Corresponding standard deviation curves over 100 different phase realizations and averaged SR curves are presented in Fig 5(b) to Fig 8(b) and Fig 5(c) to Fig 8(c) All MR curves have converged after complete iterations in Fig 5(a) to Fig 8(a) From Fig 5(b) to Fig 8(b), we can see that SA, GA and Alopex have relatively smaller standard deviations than SPGD, which shows that SA, GA and Alopex have stronger adaptability to different turbulence realizations than SPGD The averaged SR’s of these four different control algorithms are very close to each other in Fig 5(c) to Fig 8(c), which indicates SA, GA, SPGD and Alopex have almost equal correction ability under D / r0  10 284 Simulated Annealing Theory with Applications (a) (b) (c) Fig Adaptation process of adaptive optics system when SA is used as the control algorithm (a): averaged curves of MR, (b): the standard deviation curve of MR over 100 different phase realizations and (c): averaged SR curves during 4000 iterations (a) (b) (c) Fig Adaptation process of adaptive optics system when GA is used as the control algorithm (a): averaged curves of MR, (b): the standard deviation curve of MR over 100 different phase realizations and (c): averaged SR curves during 500 generations (a) (b) (c) Fig Adaptation process of adaptive optics system when SPGD is used as the control algorithm (a): averaged curves of MR, (b): the standard deviation curve of MR over 100 different phase realizations and (c): averaged SR curves(c) during 2000 iterations Simulated Annealing for Control of Adaptive Optics System 285 (a) (b) (c) Fig Adaptation process of adaptive optics system when Alopex is used as the control algorithm (a): averaged curves of MR, (b): the standard deviation curve of MR over 100 different phase realizations and (c): averaged SR curves during 4000 iterations Fig gives the averaged focal spot when SA, GA, SPGD and Alopex are use as the control algorithm of the AO system respectively For purposes of comparison, we also fit the 61element deformable mirror figure to the phase screens using least squares to obtain the best correction achievable with the given 61-element DM (a) SA (b) GA (c) (d) < 286 Simulated Annealing Theory with Applications (e) (f) Fig Comparison of focal spots before correction (a) and after correction with SA (c), with GA (d), with SPGD (e) and with Alopex (f) ; (b) is the averaged focal spot of the residual wave-front with the least squares fitting From Fig 9, we can get these four different algorithms have strong ability to atmospheric turbulence when D / r0 is 10 Compared with the least squares fitting, they almost obtain the best correction achievable for the 61-element DM 4.3 Analysis of averaged convergence speed The convergence speed is an important criterion on which the algorithm can be applied to realtime adaptive optics system Fig (a) to Fig 8(a) give the averaged curves of MR over 100 different phase realizations The abscissa in Fig 5(a) to Fig 8(a) is the iteration number of algorithm for SA, SPGD and Alopex and the number of evolution generation for GA It seems that GA has the rapidest speed from the averaged curves of MR because of its fewer evolution generation This result is not true because the number of small perturbations sent to the system per iteration is different for different algorithms From the introduction to the basic idea of several algorithms in section 3.5, we know that SA and Alopex need one perturbation per iteration; SPGD needs two perturbations per iteration and GA needs 100 perturbations per generation Note that the number of perturbation in GA bears on the number of the population size To reduce the number of perturbation, one can choose a relative small population size but the convergence of system will need more generations The related analysis can refer to section 4.1 We use the number of small perturbations not the number of iteration or generation to estimate the averaged convergence speed of different algorithms Consulting results in Fig 5(a) to Fig 8(a) and above analysis, we make use of the number of small perturbations needed by achieving the 80% of the range of MR during the adaptation process under control of different algorithms Corresponding data are in Table SA GA SPGD Alopex Value of 80% MR Range 0.54 0.548 0.524 0.532 Iterations or Generations 767 143 464 1609 Perturbations 767 143*100=14300 464*2=928 1609 Table Comparison of the number of small perturbations sent to the system when the adaptive optics system achieves the 80% of the range of MR during the adaptation process under control of different algorithms Simulated Annealing for Control of Adaptive Optics System 287 The value of MR is 0.54 for SA, 0.548 for GA, 0.524 for SPGD and 0.532 for Alopex respectively in Table These data show the range of MR of different algorithms are close to each other because their start value of MR is the same The number of iterations or generations is 767 for SA, 143 for GA, 464 for SPGD and 1609 for Alopex but the number of small perturbation is 767 for SA, 14300 for GA, 928 for SPGD and 1609 for Alopex respectively These data show GA is the slowest algorithm and the number of pertubations is almost as 20 times as that of SA, 15 times as SPGD and times as Alopex, while SA is the fastest algorithm becauese of its the fewest perturbations The advantage of GA is that it is far more likely that the global extremum will be found, as the data shown in second column in Table 1; the disadvantage is that if often takes a long time to converge Above simulation results express relative differences of these algorithms in convergence rate, which can offer us some references in choosing stochastic parallel optimization algorithm for real applications 4.4 Zernike order and wavefront of the same single frame phase screen Fig 10 gives Zernike coefficients 3-104 decomposed from the same phase screen when SA, GA, SPGD and Alopex are used as control algorithm of adaptive optics system respectively Corresponding wavefronts are shown in Fig 11 (a) (c) (b) (d) 288 Simulated Annealing Theory with Applications (e) (f) Fig 10 Comparison of Zernike coefficients 3-104 before correction (a) and after correction with SA (c), GA (d), SPGD (e) and Alopex (f) ; (b) is the Zernike coefficients of the residual wave-front with the least squares fitting We also fit the DM figure to the phase screen using least squares to obtain the best correction achievable with the given 61-element DM The fitting results are also shown in Fig 10 and Fig 11 The unit in Fig 10 is rad and wavelength  in colour bar of Fig 11 (a) (c) (b) (d) Simulated Annealing for Control of Adaptive Optics System 289 (e) (f) Fig 11 Comparison of wavefronts before correction (a) and after correction with SA (c), GA (d), SPGD (e) and Alopex (f); (b) is the residual wave-front with the least squares fitting The unit of colour bar is wavelength  From Fig 10 and Fig 11, we can obtain that these four different algorithms have strong ability to atmospheric turbulence when D / r0 is 10 Compared with the least squares fitting, they almost obtain the best correction achievable for the 61-element DM Conclusion We presented basic principles of Simulated Annealing, Genetic Algorithm, Stochastic Parallel Gradient Descent, and Algorithm of pattern extraction in control application of adaptive optics system Based on above stochastic parallel optimization algorithms, we simulated an adaptive optics system with a 61-element deformable mirror and compared these algorithms in convergence speed, correction capability From section 4.2 and 4.4, we can get these four different algorithms have strong ability to atmospheric turbulence when D / r0 is 10 Compared with the least squares fitting, they almost obtain the best correction achievable for the 61-element DM The correction effect of GA is litter better than other algorithms and SA is the secondly better algorithm But SA, GA and Alopex have stronger adaptability to different turbulence realizations than SPGD because of its relatively big standard deviation From section 4.3, we can conclude SA is the fastest and GA is the slowest in these algorithms The number of perturbation by GA is almost as 20 times as that of SA, 15 times as SPGD and times as Alopex GA begins with a population of candidate solutions (individuals) and evolves towards better solutions through techniques inspired by evolutionary biology (such as natural selection or mutation) Perhaps the main problem of GA is the time cost of it The algorithm may converge, and it may be a guaranteed global extremum, however, if this requires excessive a mounts of computer equipment or if it takes an unreasonable length of time to provide the solution, then it will not be suitable But if the real-time is not required by adaptive optics system in some special application fields, GA is the best choice In real applications, after the deformable mirror is established, the correction time of AO system is mainly affected by the read-out and computation time of performance metric, 290 Simulated Annealing Theory with Applications which occupies the most part time of control algorithm This point is the same in both simulation test and real AO systems Above simulation results express relative differences of these algorithms in convergence rate, which can offer us some references in choosing stochastic parallel optimization algorithm for specific applications In conclusion, we can get that each algorithm has its advantages and disadvantages from above simulation results and discussions For static or slowly changing wavefront aberrations, these algorithms all have high correction ability For dynamic wavefront aberrations, convergence rates of these algorithms are slow relative to the change rate of atmosphere turbulence They can be applied to real-time wavefront correction if being combined with high speed photo-detector, high speed data processing and high response frequency wave-front corrector More research is necessary to this problem Such as, how about these algorithms applied to much stronger turbulence conditions and much more elements deformable mirror or other kinds of wavefront corrector? 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Gaussian number with zero mean and σi standard deviation (2) Simulated Annealing Theory with Applications 2.4 Objective function The cost or objective function is an expression that, in some applications, ... solved by a variational formulation with posterior solution using the Finite Element Method (FEM) (Zienkiewicz & Morgan, 1983) 6 Simulated Annealing Theory with Applications  p(t )  M   